Ablation margin quantification after thermal ablation of malignant liver tumors: How to optimize the procedure? A systematic review of the available evidence

Introduction To minimize the risk of local tumor progression after thermal ablation of liver malignancies, complete tumor ablation with sufficient ablation margins is a prerequisite. This has resulted in ablation margin quantification to become a rapidly evolving field. The aim of this systematic review is to give an overview of the available literature with respect to clinical studies and technical aspects potentially influencing the interpretation and evaluation of ablation margins. Methods The Medline database was reviewed for studies on radiofrequency and microwave ablation of liver cancer, ablation margins, image processing and tissue shrinkage. Studies included in this systematic review were analyzed for qualitative and quantitative assessment methods of ablation margins, segmentation and co-registration methods, and the potential influence of tissue shrinkage occurring during thermal ablation. Results 75 articles were included of which 58 were clinical studies. In most clinical studies the aimed minimal ablation margin (MAM) was ≥ 5 mm. In 10/31 studies, MAM quantification was performed in 3D rather than in three orthogonal image planes. Segmentations were performed either semi-automatically or manually. Rigid and non-rigid co-registration algorithms were used about as often. Tissue shrinkage rates ranged from 7% to 74%. Conclusions There is a high variability in ablation margin quantification methods. Prospectively obtained data and a validated robust workflow are needed to better understand the clinical value. Interpretation of quantified ablation margins may be influenced by tissue shrinkage, as this may cause underestimation.


Introduction
Thermal ablation is an effective treatment for primary and secondary liver tumors [1][2][3]. For tumors of limited size (≤2 cm) thermal ablation using radiofrequency ablation (RFA) or microwave ablation (MWA) is a first line therapy, particularly in patients with co-morbidity, underlying liver cirrhosis and/or centrally located tumors. Nevertheless, surgical resection is generally considered to be more effective, as thermal ablation is associated with higher local tumor progression (LTP) rates. To minimize the risk of LTP after thermal ablation, complete tumor ablation with sufficient ablation margins is essential. The correlation between ablation margins and LTP was first demonstrated in 2008 by Kei et al. [4]. Later, this was confirmed by other large trials [5][6][7].
Most commonly, ablation margins after thermal ablation are assessed by side-by-side comparison of pre-and post-ablation crosssectional images. This method is usually based on visual assessment, i.e. eye-balling, but may be aided by two-dimensional measurements using anatomical landmarks on both scans. The use of software-assisted quantitative assessment of ablation margins has gained interest in literature over the last years [6][7][8][9]. Several studies indicate it could contribute to better determine technical success of thermal ablation treatments and estimate the risk of LTP [7][8][9]. However, there is wide variation in methods used for margin quantification and the optimal method has not yet been established.
Ablation margin quantification is performed using software with specific segmentation and image co-registration algorithms. The coregistration algorithms may differ by design as co-registration can be performed either in a rigid or non-rigid way. In rigid co-registration, the images are registered using only rotation and translation of the images whereas non-rigid co-registration also allows deformation of the images. Besides the differences between rigid and non-rigid co-registration, the co-registration could be performed manually, semi-automatically or fully automatically. Other differences may be with respect to volume of interest selection or usage of landmarks.
Besides the more technical variety among co-registration algorithms, patient and treatment related factors may affect the result of ablation margin quantification. Differences in respiration mode and patient positioning may cause considerable variation in the shape and position of the liver between the pre-and post-ablation scans. Moreover, tissue shrinkage as a direct result of tissue heating possesses an important challenge on ablation margin interpretation [10]. As the ablated tissue tends to shrink during thermal ablation, the ablation margins may be underestimated. Unfortunately, the degree and direction of tissue shrinkage is unpredictable [10].
Quantitative ablation margin assessment holds great promise as a tool to better predict patients at risk for LTP after thermal ablation. The aim of this systematic review is to create an overview of the current evidence with respect to qualitative and quantitative evaluation methods of ablation margins, image processing tools, and the potential influence of tissue shrinkage occurring during thermal ablation.

Search strategy
The electronic database Medline was searched on 01/02/2021 for all studies describing "image segmentation", "image registration", "ablation margins", "treatment success" or "tissue shrinkage" during treatment of liver tumors using thermal ablation techniques, i.e. "RFA" or "MWA", since 01/01/2009 as techniques have constantly been improving and the quality of ablation of > 12 years old was not considered representative. The full search term used can be found in Appendix A. Articles were sequentially evaluated based on title, abstract and full text for meeting all in-and exclusion criteria. The literature search, study selection, data extraction and study quality assessment were independently conducted by two reviewers (P.H. and F.B.). Any disagreements were resolved in consensus.

Exclusion criteria
Articles were excluded if they did not relate to percutaneous thermal ablation of malignant liver tumors with RFA or MWA; if surgical resection was performed; and if the aim of the article was to evaluate combination therapy with ablation and trans-arterial or systemic therapy. Articles related to liver segmentation or co-registration were excluded if they did not define the segmentation or co-registration method used; and if ultrasound (US), positron emission tomography (PET), or single photon emission computed tomography (SPECT) images were used for image segmentation or co-registration. Articles using hybrid imaging modalities were not excluded if tumor and/or ablation zone segmentation was performed using (contrast-enhanced) CT or MRI. Articles related to evaluation of ablation margins were excluded if they did not provide a definition for technical success or minimal ablation margins. Finally, systematic reviews, reviews, letters to the editor, conference abstracts and full-text articles in other languages than English were excluded. References of systematic reviews and reviews were evaluated for further inclusion of articles missed in the initial search.

Data extraction
For each article, the following information was extracted if present: first author, publication year, journal, study type, imaging modality, tumor type, mean tumor size, number of subjects and tumors, ablation method, software used, intended minimal ablation margin (MAM), LTP rate, method of MAM determination, segmentation method, coregistration method, other treatment success outcome measures, and validation of segmentation and registration.

Clinical studies
In total, 58 clinical studies with 4311 tumors were included in the results. RFA was the ablation method used most frequently and HCC patients (n = 3431 tumors) formed the main population in most studies. Intrahepatic cholangiocarcinoma (n = 57) and hepatic metastases from other primary origin (predominantly colorectal cancer, n = 456) were other pathologies included. All studies were performed in a single center and most of them had a retrospective study design. A high variety in population size was found (7-211, median: 36.5). Mean or median lesion sizes were < 30 mm for all clinical studies. An overview of all included clinical studies can be found in Table 1.
In all studies, the MAM was expressed as the smallest distance from the tumor boundary to the nearest border of the ablation zone. In general, the intended MAM was ≥ 5 mm, as can be seen in Table 1. In a few studies additional quantification measures were used, such as the coverage of the tumor by the ablation zone, or the extent that a 5 mm ablation margin was reached in all directions. In 36 studies, the quantified MAM was correlated with the occurrence of LTP. Fig. 2 shows the correlation between the intended MAM and the occurrence of LTP. In one study immunohistology of a post-ablation biopsy was correlated with the occurrence of LTP [35]. Table 3 describes the different methods used for segmentation of the tumor and ablation zone. Semi-automatic segmentation methods were used in 12/13 studies [8,9,22,31,[37][38][39][40][41][42][43][44] and manual segmentation was used in only one study [45]. Semi-automatic segmentation methods used included edge detection [8,9,44], region growing based algorithms [22,40,41,45], and machine learning based algorithms involving classification [39] and clustering [37,38,42]. In four of these papers in-house segmentation software was used [37][38][39]42] and in the other studies commercially available software [8,9,22,31,40,41,43,44].

Tissue shrinkage
Tissue shrinkage was evaluated using ex vivo bovine or porcine livers [10,[55][56][57][58][59][60], in vivo porcine livers [61,62] or pre-and post-ablation imaging of patients with HCC or metastases [63]. In the ex vivo animal models, the liver was divided in test samples, after which ablation was performed using either RFA or MWA. In the in vivo animal model, the ablation was performed in different segments of the liver. The samples consisted of normal liver parenchyma without tumors. Ablation times ranged from 1 min to 20 min, with power settings between 20 and 200 W. Tissue shrinkage was measured through the dimensions of the samples pre-and post-ablation, or the displacement of markers inserted into the tissue sample. Tissue shrinkage was expressed as the contraction ratio, or contraction measured in percentage, see Table 5. Noteworthy, in the study by Weiss et al. the contraction was expressed as planar strain, which showed tissue dilatiation for ablation times < 10 min [59].

Discussion
Ablation margin quantification has been a topic of high interest in literature. In this systematic review, we have evaluated clinical study methodology, MAM quantification software methods, imaging coregistration methods, segmentation methods and tissue shrinkage. In general, a high variety in methodology was found between different studies.
With respect to the clinical studies, a MAM of ≥ 5 mm was intended mostly, in accordance with ablation guidelines [81]. Although the studies were very heterogeneous, and only limited data were available of studies with an intended MAM of ≥ 3 mm and ≥ 10 mm, LTP rates tended to decrease at larger intended MAM.
In studies that aimed at retrospective quantification of the ablation margins, the properties of the ablation margin quantification tools or software were evaluated. The MAM (i.e. smallest distance between outer boundaries of tumor and ablation zone) was the outcome measure used in all studies. Only 3 studies used other additional outcome measures, such as ablation surface area or volumetric data. In a limited number of studies, the MAM could also be quantified in 3D rather than the standard orthogonal image planes. With the emerging field of dedicated ablation margin quantification software and incorporation of ablation margin quantification in clinical trials, it is expected that this more thorough analysis will become the new standard.
Segmentation of tumor and ablation zone plays a major role in objectively quantifying ablation margins. Several segmentation algorithms were used in the included studies, most of them were semiautomatic and based on underlying grey-scale or region-growing algorithms. Multiple methods were used to validate segmentations among different interpreters or against a golden standard. Although the results of these validations are not directly comparable, the overall performance seems good. To be better able to compare the robustness and accuracy of each segmentation tool, a standardized validation method would be needed, despite their specific advantages and disadvantages. The DSC is suitable for comparing two segmentations based on their overlap, but its sensitivity is dependent on the size of the segmented structure. Besides the technical aspects of segmentation, several clinical implications should be taken into consideration. The size and shape of a tumor may appear differently on arterial and venous phases. Choosing the right scan phase is therefore crucial for obtaining the correct ablation margin. Moreover, for a smooth incorporation in the clinical workflow it is important that segmentation algorithms are fast, accurate and easily correctable.
Image co-registration between pre-and post-ablation imaging is the basis for quantifying distances between boundaries of the tumor and ablation zone. Rigid and non-rigid co-registration techniques were used about as often and most of the co-registration methods included in this systematic review were semi-automatically. Non-rigid co-registration algorithms usually result in visually better outcomes for the entire liver, as deformational differences of the liver between the pre-and postablation scans are adjusted for. However, local tissue deformations as a result of thermal ablation may result in inaccurate MAM measurements. Luu et al. proposed to manually penalize local areas with large erroneous non-rigid deformations by enforcing local rigidity [50]. Similarly, Passera et al. replaced these local areas with synthetic patterns to be able to use a non-rigid co-registration approach without the undesired, erroneous deformations in the ablation zone that hamper correct MAM measurements [42]. Locally optimized co-registration between pre-and post-ablation imaging in the tumor region is the main objective. The use of local landmark placement is possible in many co-registration algorithms and may be used for this sake.
To reduce co-registration errors in a clinical setting, the pre-and post-ablation imaging are best obtained during the ablation procedure with the patient in an identical bed position and with a similar inhalation mode. Although thermal ablation could be performed using intravenous sedation, general anesthesia has the advantage of being able to use high-frequency jet ventilation or breath hold [81]. This may help reducing differences in inhalation mode, and therefore co-registration errors. It has yet to be established which scanning protocol and phase is most suitable for accurate and reproducible quantification of ablation margins.
Tissue shrinkage during ablation may be of high influence on the outcome of ablation margin quantification, with substantial tissue shrinkage rates reported in animal studies. As a result of tissue shrinkage, ablation margins may be underestimated. During the followup after thermal ablation, the ablation zone may shrink further on imaging [82]. Therefore, ablation margin quantification should be determined based on images acquired directly after treatment. This systematic review only included articles using CECT or MRI for immediate ablation margin evaluation. For clinical purpose, hybrid imaging with PET-CT or PET-MRI may help identifying patients at risk of developing LTP [83]. Besides direct tissue shrinkage during ablation, local edema around the ablation zone may cause the opposite effect directly surrounding the ablation necrosis, and my influence image co-registration. The evidence available on the use of ablation margin quantification is currently based on retrospective studies with a high variability in study methodology. Both clinical factors and technical factors, in terms of image acquisition, reconstruction algorithms, and image processing play major roles in the quantification of ablation margins. A better understanding is needed of how these factors affect the outcome, and what combination of factors results in a robust and accurate method of ablation margin quantification. With this standard at hand, the correlation between measured MAM and the occurrence of LTP could ultimately be better understood and incorporated in the standard workflow.
The combination of prospective clinical trials and technological advancements is what is needed to push ablation margin quantification to the next stage.

Conclusion
Ablation margin quantification is emerging to become a valuable tool in optimizing minimally invasive treatment of hepatic tumors. This systematic review shows that there is currently a high variability in ablation margin quantification methodology in terms of image coregistration, segmentation methods, and interpretation. Although the method for reaching the maximum precision in a robust way may still be unknown, the correct clinical use and interpretation will be very important as the ultimate goal is to interpret ablation margins at a millimeter level of accuracy. Optimization of scanning protocols, time reduction between pre-and post-ablation scans, and quality assessment of image co-registration are therefore of great importance.

External funding
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Sources of support
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Ethical statement
This systematic review study was performed without any direct patient data. It is performed in accordance with PRISMA statement for systematic reviews. All authors contributed to the article and consent submitting to European Journal of Radiology Open for publication.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.   CT-CT + + R and NR A --CT= computed tomography, MRI = magnetic resonance imaging,